This paper studies algorithms for the solution of mixed symmetric linear complementarity problems. The goal is to compute fast and approximate solutions of medium to large sized problems, such as those arising in computer game simulations and American options pricing. The paper proposes an improvement of a method described by Kocvara and Zowe [19] that combines projected Gauss-Seidel iterations with subspace minimization steps. The proposed algorithm employs a recursive subspace minimization designed to handle severely ill-conditioned problems. Numerical tests indicate that the approach is more efficient than interior-point and gradient projection methods on some physical simulation problems that arise in computer game scenarios.

Transitional expressions provide glue that holds ideas together in a text and enhance the logical organization, which together help im-prove readability of a text. However, in most current statistical machine translation (SMT) systems, the outputs of compound-complex sentences still lack proper transitional expres-sions. As a result, the translations are often hard to read and understand. To address this issue, we propose two novel models to en-courage generating such transitional expres-sions by introducing the source compound-complex sentence structure (CSS). Our models include a CSS-based translation model, which generates new CSS-based translation rules, and a generative transfer model, which en-courages producing transitional expressions during decoding. The two models are integrat-ed into a hierarchical phrase-based translation system to evaluate their effectiveness. The ex-perimental results show that significant im-provements are achieved on various test data meanwhile the translations are more cohesive and smooth. 1

This paper presents a joint study of application and architecture to improve the performance and scalability of an irregular application – computing betweenness centrality – on a many-core architecture IBM Cyclops64. The characteristics of unstructured parallelism, dynamically non-contiguous memory access and low arithmetic intensity in betweenness centrality pose an obstacle to an efficient mapping of parallel algorithms on such many-core architectures. By identifying several key architectural features, we propose and evaluate efficient strategies for achieving scalability on a massive multi-threading many-core architecture. We demonstrate several optimization strategies including multi-grain parallelism, just-in-time locality with explicit memory hierarchy and non-preemptive thread execution, and fine-grain data synchronization. Comparing with a conventional parallel algorithm, we get 4X-50X improvement in performance and 16X improvement in scalability on a 128-cores IBM Cyclops64 simulator. i